Computational approach to the discovery of potential neprilysin inhibitors compounds for cardiovascular diseases treatment

Neprilysin (NEP, EC: 3.4.24.11) is an enzymatic membrane protein considered the prototype of the M13 zinc metallopeptidase family. Inhibitors of this enzyme constitute therapeutic targets for cardiovascular diseases. In spite of the existing alternatives for the treatment of these pathologies, the quality of life and the prognosis of the patients remain precarious, so the discovery of new drugs is required for this purpose. In this sense, in silico methods play a fundamental role, since they are an alternative to the traditional "trial and error" methods for obtaining new molecular entities. Taking into account that the main motivation of this work is to propose theoretical models, for the identification of new Neprilysin inhibitor compounds through a virtual screening system, QSAR-LDA and QSAR-MLP models were developed with accuracy >80%. Previously, a threshold was established for the inhibition of NEP using the piecewise regression method (pIC(50) = 6.855), and from this value, two classes of compounds (potent inhibitors and poor/moderate inhibitors) were formed. The developed models were used for the virtual screening of compounds, establishing as rules: that they are predicted as active by both models, that they are within the domain of application, and that they possess drug-like properties for oral administration. Following this strategy, eight potential Neprilysin inhibitor compounds were identified, concluding that the proposed computational tools constitute an efficient method for the identification of new inhibitor drugs for this enzyme.